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Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task.
A coefficient of agreement for nominal scales
Jacob Cohen. 1960 · 1960
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Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang. 2002 · 2002
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Named entity recognition in Wikipedia
Dominic Balasuriya, Nicky Ringland, Joel Nothman, Tara Murphy, and James R. Curran. 2009 · 2009
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Named entity recognition in tweets: An experimental study
Alan Ritter, Sam Clark, Mausam, and Oren Etzioni. 2011 · 2011
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Few-shot named entity recognition: A comprehensive study
Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, and Jiawei Han. 2020 · 2012
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Fine-grained entity recognition
Xiao Ling and Daniel S. Weld. 2012 · 2012
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Ontonotes release 5.0 ldc2013t19
Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013 · 2013
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Context-dependent fine-grained entity type tagging
Dan Gillick, Nevena Lazic, Kuzman Ganchev, Jesse Kirchner, and David Huynh. 2014 · 2014
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Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/uthealth corpus
Amber Stubbs and Özlem Uzuner. 2015 · 2014
Earlier work this paper cites.
Named entity recognition with bidirectional LSTM-CNNs
Jason P.C. Chiu and Eric Nichols. 2016 · 2016
Earlier work this paper cites.
Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
Earlier work this paper cites.
End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
Xuezhe Ma and Eduard Hovy. 2016 · 2016
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Kbqa: learning question answering over qa corpora and knowledge bases
Wanyun Cui, Yanghua Xiao, Haixun Wang, Yangqiu Song, Seung-won Hwang, and Wei Wang. 2017 · 2017
Cited alongside, same era.
Results of the WNUT2017 shared task on novel and emerging entity recognition
Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham. 2017 · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017 · 2017
Cited alongside, same era.
Ultra-fine entity typing
Eunsol Choi, Omer Levy, Yejin Choi, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Few-shot classification in named entity recognition task
Alexander Fritzler, Varvara Logacheva, and Maksim Kretov. 2019 · 2019
Later among the works it cites.
Large-scale few-shot learning: Knowledge transfer with class hierarchy
Aoxue Li, Tiange Luo, Zhiwu Lu, Tao Xiang, and Liwei Wang. 2019a · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Later among the works it cites.
NNE: A dataset for nested named entity recognition in English newswire
Nicky Ringland, Xiang Dai, Ben Hachey, Sarvnaz Karimi, Cecile Paris, and James R. Curran. 2019 · 2019
Later among the works it cites.
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Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
Cited alongside, same era.
Few-shot learning for named entity recognition in medical text
Maximilian Hofer, Andrey Kormilitzin, Paul Goldberg, and Alejo Nevado-Holgado. 2018 · 2018
Cited alongside, same era.
HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019a · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019b · 2019
Cited alongside, same era.
Event detection with trigger-aware lattice neural network
Ning Ding, Ziran Li, Zhiyuan Liu, Haitao Zheng, and Zibo Lin. 2019 · 2019
Cited alongside, same era.
Few-shot named entity recognition via meta-learning
Jing Li, Billy Chiu, Shanshan Feng, and Hao Wang. 2020a
Cited in the paper.
Few-shot slot tagging with collapsed dependency transfer and label-enhanced task-adaptive projection network
Yutai Hou, Wanxiang Che, Yongkui Lai, Zhihan Zhou, Yijia Liu, Han Liu, and Ting Liu. 2020 · 2020
Later among the works it cites.
Modeling relation paths for knowledge graph completion
Ying Shen, Ning Ding, Hai-Tao Zheng, Yaliang Li, and Min Yang. 2020 · 2020
Later among the works it cites.
MAVEN: A Massive General Domain Event Detection Dataset
Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, and Jie Zhou. 2020 · 2020
Later among the works it cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Later among the works it cites.
Simple and effective few-shot named entity recognition with structured nearest neighbor learning
Yi Yang and Arzoo Katiyar. 2020 · 2020
Later among the works it cites.
Prototypical representation learning for relation extraction
Ning Ding, Xiaobin Wang, Yao Fu, Guangwei Xu, Rui Wang, Pengjun Xie, Ying Shen, Fei Huang, Hai-Tao Zheng, and Rui Zhang. 2021 · 2021
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